Smart Energy Management via Network Slicing and Usage Prediction
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Solution Overview
Problem
Existing energy management systems lack effective tools for users to monitor and act on detailed energy consumption data, hindering efficient energy usage and management.
Innovation Solution
A system and method utilizing processors, sensors, and blockchain-based platforms to record and analyze energy consumption data, predict future energy needs, and provide personalized recommendations for devices via network slices, incorporating machine learning and smart contracts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced metering systems like smart meters are deployed to record energy consumption in greater detail, then measurement precision of energy usage is improved, but device complexity and loss of information increase due to the lack of effective user tools to monitor and act upon the data
Solution Approach 1:
The patent introduces an energy management system comprising a processor and memory that acts as an intermediary between smart meters and users. This system receives detailed energy consumption data from smart meters, processes it through machine learning models to generate insights and predictions, and delivers actionable recommendations to users via communication interfaces, thereby bridging the gap between precise measurement and user understanding
Solution Approach 2:
The system enables users to self-monitor and self-manage their energy consumption by providing them with processed insights, predictions, and actionable recommendations. Users can independently make informed decisions about their energy usage patterns without requiring complex manual analysis of raw meter data
2Adaptability or versatility
If detailed energy consumption data is collected and processed to provide personalized energy management services, then adaptability and user empowerment are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the energy management system into distinct functional modules: data collection from smart meters, machine learning model processing, insight generation, and user interface communication. This modular architecture allows each component to handle specific tasks independently, reducing overall system complexity while maintaining personalized service capabilities
Solution Approach 2:
The system changes the parameters of energy data processing by applying machine learning models that transform raw consumption data into meaningful insights and predictions. This parameter transformation enables personalized energy management services without requiring proportional increases in system complexity
Data Source
AI summary
The present disclosure provides a system and a method for facilitating smart energy management services in a network. In particular, the system provides the smart energy management services to users, enterprises, and the like. The system may collect information related to operation of one or more computing devices in a network, extract one or more parameters related to one or more sources of energy based on the collected information, and provide personalized energy management services to each of the one or more computing devices in the network via network slicing. The energy management services may include one or more recommendations to be executed at the one or more computing devices.